Assessment of Surface Scattering Models Within the Water Cloud Model Toward Soil Moisture Retrievals Using Sentinel-1 and Sentinel-2 Images

The agricultural productivity and the optimized use of water resources rely on the soil moisture (SM) retrieval to achieve some of the sustainable development goals, such as ensuring food security and monitoring climate change. One of the main aspects to provide accurate SM retrieval results is the selection of the most effective models. This study is carried out to exhibit the impact of three different soil formulations [i.e., Linear, Oh, and improved integral equation model (I2EM)] within the water cloud model (WCM). The experiments are conducted based on the combined use of Sentinel-1 and Sentinel-2 images. The in-situ measurements used in this work are collected from five different fields in Huamantla, Central Mexico. The experiments focus on the complete growing season of corn taking into consideration the soil and the vegetation contribution. The best
Bias
and
unbiased root mean squared difference (ubRMSD)
values obtained by the Oh-WCM are equal to −0.437 and 0.295 dB, respectively at VV in PX1. The I2EM-WCM achieved
Bias
and
ubRMSD
values equal to −0.760 and 0.379 dB at VV, respectively. The linear-WCM also obtained low
Bias
and
ubRMSD
values equal to −0.297 and 0.322 dB, respectively. Therefore, the combination of the Oh model within the WCM is considered as the appropriate combination for the SM retrieval due to its high achieved accuracy. The sensitivity analysis of changes in
$\sigma ^{0}_{pq,\text{tot}}$
due to changes in SM found that it is possible to capture changes higher than 0.06 m
$^{3}$
/m
$^{3}$
in SM over the complete growing season of corn using C-band backscatter observations.